533 research outputs found

    Reinforcement Learning Empowered Unmanned Aerial Vehicle Assisted Internet of Things Networks

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    This thesis aims towards performance enhancement for unmanned aerial vehicles (UAVs) assisted internet of things network (IoT). In this realm, novel reinforcement learning (RL) frameworks have been proposed for solving intricate joint optimisation scenarios. These scenarios include, uplink, downlink and combined. The multi-access technique utilised is non-orthogonal multiple access (NOMA), as key enabler in this regime. The outcomes of this research entail, enhancement in key performance metrics, such as sum-rate, energy efficiency and consequent reduction in outage. For the scenarios involving uplink transmissions by IoT devices, adaptive and tandem rein forcement learning frameworks have been developed. The aim is to maximise capacity over fixed UAV trajectory. The adaptive framework is utilised in a scenario wherein channel suitability is ascertained for uplink transmissions utilising a fixed clustering regime in NOMA. Tandem framework is utilised in a scenario wherein multiple-channel resource suitability is ascertained along with, power allocation, dynamic clustering and IoT node associations to NOMA clusters and channels. In scenarios involving downlink transmission to IoT devices, an ensemble RL (ERL) frame work is proposed for sum-rate enhancement over fixed UAV trajectory. For dynamic UAV trajec tory, hybrid decision framework (HDF) is proposed for energy efficiency optimisation. Downlink transmission power and bandwidth is managed for NOMA transmissions over fixed and dynamic UAV trajectories, facilitating IoT networks. In UAV enabled relaying scenario, for control system plants and their respective remotely deployed sensors, a head start reinforcement learning framework based on deep learning is de veloped and implemented. NOMA is invoked, in both uplink and downlink transmissions for IoT network. Dynamic NOMA clustering, power management and nodes association along with UAV height control is jointly managed. The primary aim is the, enhancement of net sum-rate and its subsequent manifestation in facilitating the IoT assisted use case. The simulation results relating to aforesaid scenarios indicate, enhanced sum-rate, energy efficiency and reduced outage for UAV-assisted IoT networks. The proposed RL frameworks surpass in performance in comparison to existing frameworks as benchmarks for the same sce narios. The simulation platforms utilised are MATLAB and Python, for network modeling, RL framework design and validation

    Digital twin of construction crane and realization of the physical to virtual connection

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    Digital twin is an integrated multi-physics representation of a complex physical entity. This article constructs the digital twin of the construction crane, proposes a framework for the construction of the tower crane digital twin, and realizes the connection from physical to virtual in the concept of digital twin. The main contributions are divided into three parts: development of tower crane monitoring dataset, tower crane detection and tower crane operation mode recognition. By using labellmg to annotate more than 20,000 tower crane images in 583 tower crane videos, a tower crane image recognition dataset and a tower crane operating mode dataset are established. Yolov5x algorithm is selected in the tower crane detection. Edge extraction is used to improve the quality of the raw dataset and distance-intersection-over union non-maximum suppression is used to replace the traditional non-maximum suppression part in the Yolov5x algorithm to improve the detect accuracy when some tower cranes are overlapping. The final test set detection accuracy rate is 93.85%. After comparing the LSTM and CNN algorithms, 3DResNet algorithm is selected for tower crane operational mode recognition. The raw dataset is augmented by rotating the image by ±10° and ±20°, and the augmented dataset enlarges five times. Using these methods, the final recognition accuracy of tower crane operation mode reaches 87%. These models can be installed on the cctv to monitor the running status of the tower crane in real time and transfer relevant information to the virtual model. The tower crane in the virtual space completes the action of the physical tower crane, thereby realizing the physical-to-virtual mapping in the digital twin

    Optimization of 5G Second Phase Heterogeneous Radio Access Networks with Small Cells

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    Due to the exponential increase in high data-demanding applications and their services per coverage area, it is becoming challenging for the existing cellular network to handle the massive sum of users with their demands. It is conceded to network operators that the current wireless network may not be capable to shelter future traffic demands. To overcome the challenges the operators are taking interest in efficiently deploying the heterogeneous network. Currently, 5G is in the commercialization phase. Network evolution with addition of small cells will develop the existing wireless network with its enriched capabilities and innovative features. Presently, the 5G global standardization has introduced the 5G New Radio (NR) under the 3rd Generation Partnership Project (3GPP). It can support a wide range of frequency bands (<6 GHz to 100 GHz). For different trends and verticals, 5G NR encounters, functional splitting and its cost evaluation are well-thought-out. The aspects of network slicing to the assessment of the business opportunities and allied standardization endeavours are illustrated. The study explores the carrier aggregation (Pico cellular) technique for 4G to bring high spectral efficiency with the support of small cell massification while benefiting from statistical multiplexing gain. One has been able to obtain values for the goodput considering CA in LTE-Sim (4G), of 40 Mbps for a cell radius of 500 m and of 29 Mbps for a cell radius of 50 m, which is 3 times higher than without CA scenario (2.6 GHz plus 3.5 GHz frequency bands). Heterogeneous networks have been under investigation for many years. Heterogeneous network can improve users service quality and resource utilization compared to homogeneous networks. Quality of service can be enhanced by putting the small cells (Femtocells or Picocells) inside the Microcells or Macrocells coverage area. Deploying indoor Femtocells for 5G inside the Macro cellular network can reduce the network cost. Some service providers have started their solutions for indoor users but there are still many challenges to be addressed. The 5G air-simulator is updated to deploy indoor Femto-cell with proposed assumptions with uniform distribution. For all the possible combinations of apartments side length and transmitter power, the maximum number of supported numbers surpassed the number of users by more than two times compared to papers mentioned in the literature. Within outdoor environments, this study also proposed small cells optimization by putting the Pico cells within a Macro cell to obtain low latency and high data rate with the statistical multiplexing gain of the associated users. Results are presented 5G NR functional split six and split seven, for three frequency bands (2.6 GHz, 3.5GHz and 5.62 GHz). Based on the analysis for shorter radius values, the best is to select the 2.6 GHz to achieve lower PLR and to support a higher number of users, with better goodput, and higher profit (for cell radius u to 400 m). In 4G, with CA, from the analysis of the economic trade-off with Picocell, the Enhanced multi-band scheduler EMBS provide higher revenue, compared to those without CA. It is clearly shown that the profit of CA is more than 4 times than in the without CA scenario. This means that the slight increase in the cost of CA gives back more than 4-time profit relatively to the ”without” CA scenario.Devido ao aumento exponencial de aplicações/serviços de elevado débito por unidade de área, torna-se bastante exigente, para a rede celular existente, lidar com a enormes quantidades de utilizadores e seus requisitos. É reconhecido que as redes móveis e sem fios atuais podem não conseguir suportar a procura de tráfego junto dos operadores. Para responder a estes desafios, os operadores estão-se a interessar pelo desenvolvimento de redes heterogéneas eficientes. Atualmente, a 5G está na fase de comercialização. A evolução destas redes concretizar-se-á com a introdução de pequenas células com aptidões melhoradas e características inovadoras. No presente, os organismos de normalização da 5G globais introduziram os Novos Rádios (NR) 5G no contexto do 3rd Generation Partnership Project (3GPP). A 5G pode suportar uma gama alargada de bandas de frequência (<6 a 100 GHz). Abordam-se as divisões funcionais e avaliam-se os seus custos para as diferentes tendências e verticais dos NR 5G. Ilustram-se desde os aspetos de particionamento funcional da rede à avaliação das oportunidades de negócio, aliadas aos esforços de normalização. Exploram-se as técnicas de agregação de espetro (do inglês, CA) para pico células, em 4G, a disponibilização de eficiência espetral, com o suporte da massificação de pequenas células, e o ganho de multiplexagem estatística associado. Obtiveram-se valores do débito binário útil, considerando CA no LTE-Sim (4G), de 40 e 29 Mb/s para células de raios 500 e 50 m, respetivamente, três vezes superiores em relação ao caso sem CA (bandas de 2.6 mais 3.5 GHz). Nas redes heterogéneas, alvo de investigação há vários anos, a qualidade de serviço e a utilização de recursos podem ser melhoradas colocando pequenas células (femto- ou pico-células) dentro da área de cobertura de micro- ou macro-células). O desenvolvimento de pequenas células 5G dentro da rede com macro-células pode reduzir os custos da rede. Alguns prestadores de serviços iniciaram as suas soluções para ambientes de interior, mas ainda existem muitos desafios a ser ultrapassados. Atualizou-se o 5G air simulator para representar a implantação de femto-células de interior com os pressupostos propostos e distribuição espacial uniforme. Para todas as combinações possíveis do comprimento lado do apartamento, o número máximo de utilizadores suportado ultrapassou o número de utilizadores suportado (na literatura) em mais de duas vezes. Em ambientes de exterior, propuseram-se pico-células no interior de macro-células, de forma a obter atraso extremo-a-extremo reduzido e taxa de transmissão dados elevada, resultante do ganho de multiplexagem estatística associado. Apresentam-se resultados para as divisões funcionais seis e sete dos NR 5G, para 2.6 GHz, 3.5GHz e 5.62 GHz. Para raios das células curtos, a melhor solução será selecionar a banda dos 2.6 GHz para alcançar PLR (do inglês, PLR) reduzido e suportar um maior número de utilizadores, com débito binário útil e lucro mais elevados (para raios das células até 400 m). Em 4G, com CA, da análise do equilíbrio custos-proveitos com pico-células, o escalonamento multi-banda EMBS (do inglês, Enhanced Multi-band Scheduler) disponibiliza proveitos superiores em comparação com o caso sem CA. Mostra-se claramente que lucro com CA é mais de quatro vezes superior do que no cenário sem CA, o que significa que um aumento ligeiro no custo com CA resulta num aumento de 4-vezes no lucro relativamente ao cenário sem CA

    Resource Management and Backhaul Routing in Millimeter-Wave IAB Networks Using Deep Reinforcement Learning

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    Thesis (PhD (Electronic Engineering))--University of Pretoria, 2023..The increased densification of wireless networks has led to the development of integrated access and backhaul (IAB) networks. In this thesis, deep reinforcement learning was applied to solve resource management and backhaul routing problems in millimeter-wave IAB networks. In the research work, a resource management solution that aims to avoid congestion for access users in an IAB network was proposed and implemented. The proposed solution applies deep reinforcement learning to learn an optimized policy that aims to achieve effective resource allocation whilst minimizing congestion and satisfying the user requirements. In addition, a deep reinforcement learning-based backhaul adaptation strategy that leverages a recursive discrete choice model was implemented in simulation. Simulation results where the proposed algorithms were compared with two baseline methods showed that the proposed scheme provides better throughput and delay performance.Sentech Chair in Broadband Wireless Multimedia Communications.Electrical, Electronic and Computer EngineeringPhD (Electronic Engineering)Unrestricte

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

    Теорія систем мобільних інфокомунікацій. Системна архітектура

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    Навчальний посібник містить опис логічних та фізичних структур, процедур, алгоритмів, протоколів, принципів побудови і функціонування мереж стільникового мобільного зв’язку (до 3G) і мобільних інфокомунікацій (4G і вище), приділяючи увагу розгляду загальних архітектур мереж операторів мобільного зв’язку, їх управління і координування, неперервності еволюції розвитку засобів функціонування і способів надання послуг таких мереж. Посібник структурно має сім розділів і побудований так, що складність матеріалу зростає з кожним наступним розділом. Навчальний посібник призначено для здобувачів ступеня бакалавра за спеціальністю 172 «Телекомунікації та радіотехніка», буде також корисним для аспірантів, наукових та інженерно-технічних працівників за напрямом інформаційно-телекомунікаційних систем та технологій.The manual contains a description of the logical and physical structures, procedures, algorithms, protocols, principles of construction and operation of cellular networks for mobile communications (up to 3G) and mobile infocommunications (4G and higher), paying attention to the consideration of general architectures of mobile operators' networks, their management, and coordination, the continuous evolution of the development of the means of operation and methods of providing services of such networks. The manual has seven structural sections and is structured in such a way that the complexity of the material increases with each subsequent chapter. The textbook is intended for applicants for a bachelor's degree in specialty 172 "Telecommunications and Radio Engineering", and will also be useful to graduate students, and scientific and engineering workers in the direction of information and telecommunication systems and technologies

    Machine learning enabled millimeter wave cellular system and beyond

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    Millimeter-wave (mmWave) communication with advantages of abundant bandwidth and immunity to interference has been deemed a promising technology for the next generation network and beyond. With the help of mmWave, the requirements envisioned of the future mobile network could be met, such as addressing the massive growth required in coverage, capacity as well as traffic, providing a better quality of service and experience to users, supporting ultra-high data rates and reliability, and ensuring ultra-low latency. However, due to the characteristics of mmWave, such as short transmission distance, high sensitivity to the blockage, and large propagation path loss, there are some challenges for mmWave cellular network design. In this context, to enjoy the benefits from the mmWave networks, the architecture of next generation cellular network will be more complex. With a more complex network, it comes more complex problems. The plethora of possibilities makes planning and managing a complex network system more difficult. Specifically, to provide better Quality of Service and Quality of Experience for users in the such network, how to provide efficient and effective handover for mobile users is important. The probability of handover trigger will significantly increase in the next generation network, due to the dense small cell deployment. Since the resources in the base station (BS) is limited, the handover management will be a great challenge. Further, to generate the maximum transmission rate for the users, Line-of-sight (LOS) channel would be the main transmission channel. However, due to the characteristics of mmWave and the complexity of the environment, LOS channel is not feasible always. Non-line-of-sight channel should be explored and used as the backup link to serve the users. With all the problems trending to be complex and nonlinear, and the data traffic dramatically increasing, the conventional method is not effective and efficiency any more. In this case, how to solve the problems in the most efficient manner becomes important. Therefore, some new concepts, as well as novel technologies, require to be explored. Among them, one promising solution is the utilization of machine learning (ML) in the mmWave cellular network. On the one hand, with the aid of ML approaches, the network could learn from the mobile data and it allows the system to use adaptable strategies while avoiding unnecessary human intervention. On the other hand, when ML is integrated in the network, the complexity and workload could be reduced, meanwhile, the huge number of devices and data could be efficiently managed. Therefore, in this thesis, different ML techniques that assist in optimizing different areas in the mmWave cellular network are explored, in terms of non-line-of-sight (NLOS) beam tracking, handover management, and beam management. To be specific, first of all, a procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in azimuth and elevation in non-line-of-sight mmWave communications based on a deep neural network is proposed. Moreover, along with the AOA and AOD prediction, a trajectory prediction is employed based on the dynamic window approach (DWA). The simulation scenario is built with ray tracing technology and generate data. Based on the generated data, there are two deep neural networks (DNNs) to predict AOA/AOD in the azimuth (AAOA/AAOD) and AOA/AOD in the elevation (EAOA/EAOD). Furthermore, under an assumption that the UE mobility and the precise location is unknown, UE trajectory is predicted and input into the trained DNNs as a parameter to predict the AAOA/AAOD and EAOA/EAOD to show the performance under a realistic assumption. The robustness of both procedures is evaluated in the presence of errors and conclude that DNN is a promising tool to predict AOA and AOD in a NLOS scenario. Second, a novel handover scheme is designed aiming to optimize the overall system throughput and the total system delay while guaranteeing the quality of service (QoS) of each user equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the reinforcement learning (RL) algorithm and optimization theory. An RL algorithm known as multi-agent proximal policy optimization (MAPPO) plays a role in determining handover trigger conditions. Further, an optimization problem is proposed in conjunction with MAPPO to select the target base station and determine beam selection. It aims to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. Third, a multi-agent RL-based beam management scheme is proposed, where multiagent deep deterministic policy gradient (MADDPG) is applied on each small-cell base station (SCBS) to maximize the system throughput while guaranteeing the quality of service. With MADDPG, smart beam management methods can serve the UEs more efficiently and accurately. Specifically, the mobility of UEs causes the dynamic changes of the network environment, the MADDPG algorithm learns the experience of these changes. Based on that, the beam management in the SCBS is optimized according the reward or penalty when severing different UEs. The approach could improve the overall system throughput and delay performance compared with traditional beam management methods. The works presented in this thesis demonstrate the potentiality of ML when addressing the problem from the mmWave cellular network. Moreover, it provides specific solutions for optimizing NLOS beam tracking, handover management and beam management. For NLOS beam tracking part, simulation results show that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2. Further, when it comes to the handover optimization part, the numerical results show the system throughput and delay are improved by 10% and 25%, respectively, when compared with two typical RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Deep Q-learning (DQL). Lastly, when it considers the intelligent beam management part, numerical results reveal the convergence performance of the MADDPG and the superiority in improving the system throughput compared with other typical RL algorithms and the traditional beam management method

    Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

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    The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

    Modelo de correlación QoS-QoE en un ambiente de aprovisionamiento de servicio de telecomunicaciones OTT-Telco

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    ANTECEDENTES El aprovisionamiento de la Calidad de la Experiencia (QoE) en servicios de telecomunicaciones requiere de sistemas de gestión que permitan monitorizar y controlar la QoE de los usuarios luego de consumir diferentes servicios de internet provistos sobre la red del operador. En efecto, el consumo elevado de datos por parte de los usuarios requiere, a nivel de gestión de la red, la asignación de recursos suficientes para el correcto funcionamiento de los servicios. En particular, la configuración de la Calidad del Servicio (QoS) ofrecida por el operador dentro de su dominio de operación se torna fundamental para proveer un tratamiento apropiado del tráfico, permitiendo que la percepción de la calidad del servicio por parte de los usuarios finales pueda mantenerse dentro del umbral de tolerancia de acuerdo con las políticas establecidas por la compañía de telecomunicaciones (Telco). En consecuencia, un modelo de correlación QoS-QoE es clave en el aprovisionamiento de servicios de internet sobre la infraestructura del operador de telecomunicaciones. OBJETIVOS La presente tesis de doctorado se centra en proponer un modelo de correlación QoS-QoE en un ambiente de aprovisionamiento de servicios de telecomunicaciones OTT-Telco. Para ello, cinco acciones generales deben llevarse a cabo; a saber: () caracterizar los parámetros de QoS que mayor efecto tienen en la degradación de servicios OTT. () determinar las características, condiciones, parámetros y medidas de QoE en la prestación de un servicio OTT. () establecer las condiciones y restricciones de prestación de un servicio OTT en la infraestructura de una Telco que mantenga una buena relación QoS-QoE. () desarrollar un mecanismo de estimación o predicción de QoE con base en los factores de influencia de QoS que afectan la prestación de un servicio OTT. () evaluar experimentalmente el modelo de correlación QoE-QoS. MÉTODOS Para el cumplimiento de los objetivos, se definió un modelo integrado por un macro-componente Conceptualización y otro Operacional. El macro-componente Conceptualización está orientado por el referente metodológico para la construcción de marcos conceptuales de Jabareen, y el macro-componente Operacional está alineado con las fases definidas para el desarrollo de proyectos de minería de datos, CRISP-DM. Adicionalmente, se emplearon diseños de comprobación para los algoritmos, con el fin de comprobar la validez del modelo de estimación basado en algoritmos de aprendizaje automático; es decir, el modelo de estimación fue evaluado a partir de un diseño de comprobación donde se definen, para cada uno de los algoritmos, los parámetros iniciales de operación, las configuraciones de las diferentes pruebas, y las métricas usadas para evaluar su desempeño. RESULTADOS Los resultados más importantes alcanzados son los siguientes: un mapa estratégico del estado de la ciencia en el aprovisionamiento de la QoE para servicios OTT, una conceptualización de los perfiles del modelo de correlación, un modelo matemático para la valoración de la QoE de acuerdo con el comportamiento de consumo de los usuarios, un conjunto de datos de tráfico etiquetado que relaciona el comportamiento de la red con la percepción de la calidad de los usuarios, y un modelo de estimación de la QoE de los usuarios a partir del comportamiento de tráfico de la red. CONCLUSIONES El modelo de correlación QoS-QoE puede ser empleado en sistemas gestión de la QoE donde se requiere por parte de la Telco un diagnóstico y monitorización más objetiva de la percepción de la calidad del servicio por parte de sus usuarios dentro su red de aprovisionamiento. De igual manera, el empleo de parámetros adicionales de contexto de usuario enriquecería los sistemas de gestión de la QoE en el aprovisionamiento de servicios OTT.BACKGROUND Quality of Experience (QoE) provisioning requires robust QoE-centric network and application management on Telco network for providing internet services. Indeed, traffic growth over Telco network demands resource allocation for service well performance. Particularly, Quality of Service (QoS) configuration offered by network provider operational domain becomes a key component for traffic control in a proper manner. Hence, the quality of services perceived can be managed within a tolerance threshold according to telecom operator policies. Therefore, a QoS-QoE correlational model for internet services provisioning over the telecom operator infrastructure is required. AIMS The doctoral thesis is focused on propose a correlation QoS-QoE model for provisioning telecommunications services in OTT-Telco context. To this end, five goals must be accomplishing. () To characterize QoS parameters that more impact have on OTT services performance. () To determinate QoE assumptions, features, parameters, and metrics for OTT service provisioning. () To establish the assumptions and restrictions for providing a well QoS-QoE relation in the telecom operator. () To develop an estimation model for QoE based on QoS factors in the OTT services provisioning. () To evaluate the correlation QoS-QoE model. METHODS To accomplish the aims, a model with a Conceptual and Operational macro-component was structured. The Conceptual macro-component is based on the principles for building conceptual frameworks by Jabareen, and an Operational macro-component aligned with data mining project development phases, CRISP-DM. Furthermore, test bed design was structured to validate the estimation model base on machine learning algorithms; namely, algorithms initial parameters, some tests setup, and regression metrics were determined on a test bed for validate the performance of the estimation model proposed RESULTS The most relevant results achieved are the following: a strategic science map in the QoE provisioning for OTT services, three conceptual profiles as part of the correlation QoS-QoE model, a mathematical model for QoE assessment according to user consumption behavior, a label traffic dataset that relates the traffic network with quality of services perception, and estimation QoE model for users based on traffic flows. CONCLUSIONS The QoS-QoE correlational model can be applied in QoE-Driven application and network management in which an objective controlling and monitoring of quality of services perception by users is required. Moreover, additional user context parameters could be taking account for improving the QoE management systems in OTT services provisioning.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Jesús García Herrero.- Secretario: José Armando Ordóñez Córdoba.- Vocal: Juan Carlos Cuéllar Quiñóne

    Air Traffic Management Abbreviation Compendium

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    As in all fields of work, an unmanageable number of abbreviations are used today in aviation for terms, definitions, commands, standards and technical descriptions. This applies in general to the areas of aeronautical communication, navigation and surveillance, cockpit and air traffic control working positions, passenger and cargo transport, and all other areas of flight planning, organization and guidance. In addition, many abbreviations are used more than once or have different meanings in different languages. In order to obtain an overview of the most common abbreviations used in air traffic management, organizations like EUROCONTROL, FAA, DWD and DLR have published lists of abbreviations in the past, which have also been enclosed in this document. In addition, abbreviations from some larger international projects related to aviation have been included to provide users with a directory as complete as possible. This means that the second edition of the Air Traffic Management Abbreviation Compendium includes now around 16,500 abbreviations and acronyms from the field of aviation
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